Futures

Predictions for 2024: The Rise and Challenges of Local Machine Learning, (from page 20231230.)

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Summary

The text discusses predictions for 2024 regarding local machine learning (ML) and its potential growth, driven by advancements in hardware like Apple Silicon. The author suggests that local inference may become a viable alternative to hosted inference, particularly for smaller models tailored for specific tasks. The conversation highlights the limitations of Apple Silicon compared to multi-GPU servers for large models, while acknowledging the potential for smaller, specialized models to thrive at the edge. The text also speculates on the future of local AI services, the role of various hardware manufacturers, and the possibility of innovative training pipelines using models like ONNX and WASM. The author concludes with a cautious outlook on the timeline for achieving critical mass in local ML adoption.

Signals

name description change 10-year driving-force relevancy
Rise of Local Machine Learning Local ML will gain traction due to advances in hardware like Apple Silicon. Shift from reliance on cloud-based inference to local processing on devices like phones and laptops. In 10 years, most AI applications may perform locally, enhancing privacy and reducing latency. Advancements in hardware capabilities and the need for privacy and efficiency. 4
Task-Specialized Smaller LLMs Smaller, specialized LLMs will become prevalent for personal and task-specific applications. Transition from large general models to smaller, more efficient models tailored for specific tasks. In a decade, users may mostly interact with small, task-oriented AI, improving customization and performance. The demand for efficiency and practicality in AI solutions for everyday tasks. 5
OS-Level LLM Services Operating systems may start offering centralized access to local AI models. From isolated app-based AI solutions to an integrated OS-level AI model management system. In 10 years, smartphones could seamlessly manage multiple AI models for various applications and tasks. The need for streamlined access to AI functionalities without overwhelming device storage. 4
On-Demand Mixture of Experts Models OS may enable loading expert models on demand for specific tasks. Shift from static model deployment to dynamic, on-demand model usage based on user needs. In 10 years, AI systems may intelligently adapt model usage according to real-time tasks and user behavior. The push for more efficient resource usage and tailored AI experiences. 3
Evolution of Edge Computing for AI Edge computing will evolve to support more AI capabilities on devices. From cloud-heavy AI processing to localized, edge-based AI functionalities. In a decade, edge devices could handle complex AI tasks, reducing cloud dependency and improving responsiveness. The need for real-time processing and reduced latency in AI applications. 4

Concerns

name description relevancy
Challenges of Local ML Adoption Local ML may struggle with performance due to inferior hardware capabilities compared to server-based solutions. 4
Data Privacy Risks Increased use of local inference may lead to unresolved data privacy concerns, particularly with user data being processed on personal devices. 5
Dependence on Proprietary Technology Reliance on proprietary hardware and frameworks, like Apple Silicon, could limit innovation and accessibility in local ML deployments. 4
Performance Variability The performance of local models may vary widely based on the device, affecting user experience and operational consistency. 3
Ecosystem Fragmentation Emergence of various local ML frameworks could lead to fragmentation, complicating development and integration across platforms. 3
Impact of Hardware Advancements Rapid advancements in hardware technology, such as the A17 Pro, could outpace software development, leading to potential obsolescence of existing solutions. 4
Inability to Tackle Complex Tasks Local/task-specific ML models might not handle complex tasks effectively, necessitating continuous reliance on larger cloud models. 4

Behaviors

name description relevancy
Local Machine Learning (ML) Adoption Increased reliance on local ML for inference, driven by advancements in hardware and demand for privacy and efficiency. 5
Edge Computing for AI AI models, especially smaller and task-specific ones, will increasingly run on edge devices like phones and laptops. 4
Centralized Local Model Access Operating systems may provide centralized access to local AI models to streamline usage across applications. 4
On-Demand Model Optimization Devices may fine-tune AI models based on user data during downtime, optimizing performance and personalization. 4
Shift Towards Lightweight Models The trend of utilizing smaller, specialized AI models on edge devices will gain traction, moving away from larger models. 4
Democratization of AI Hardware Efforts to make AI hardware more accessible, with potential alternatives to CUDA being explored for wider adoption. 3

Technologies

description relevancy src
Machine learning models running locally on devices, enabling real-time inference and reducing reliance on cloud infrastructure. 5 807cf4de358e6f3cbe002fddde23ea94
Artificial intelligence processing performed on devices at the edge of the network, improving response times and reducing data transfer needs. 4 807cf4de358e6f3cbe002fddde23ea94
Smaller, specialized language models optimized for specific tasks, designed to run efficiently on local devices. 4 807cf4de358e6f3cbe002fddde23ea94
A system where only necessary AI models are loaded based on usage, optimizing resource consumption and performance. 3 807cf4de358e6f3cbe002fddde23ea94
WebAssembly-based models that allow for efficient, portable AI applications across various platforms. 3 807cf4de358e6f3cbe002fddde23ea94
A distributed approach to training AI models where data remains on local devices, enhancing privacy and reducing data transfer. 4 807cf4de358e6f3cbe002fddde23ea94

Issues

name description relevancy
Local ML Adoption The rise of local machine learning (ML) driven by hardware advancements, enabling on-device inference across various devices. 4
Edge Computing for AI Increasing trend towards deploying AI models on edge devices, enhancing privacy and reducing latency. 4
Specialized Smaller LLMs Growing focus on highly specialized, quantized smaller LLMs for local and task-specific applications. 4
OS-level LLM Services Potential for operating systems to centralize access to local LLMs, streamlining model management on devices. 3
On-Demand Model Loading Emergence of on-demand model loading mechanisms within devices, allowing flexible use of AI resources based on needs. 3
Innovations in AI Training Pipelines Exploration of new methods for training and deploying AI models, including WASM-style models and ONNX. 3
Democratization of AI Hardware The need for accessible and standardized alternatives to current GPU frameworks like CUDA for broader AI adoption. 4